AUTHOR=Lv Jing , Zhou Yuhua , Jin Shengkai , Fu Chaowei , Shen Yang , Liu Bo , Li Menglu , Zhang Yuwei , Feng Ninghan 

TITLE=WGCNA-ML-MR integration: uncovering immune-related genes in prostate cancer

JOURNAL=Frontiers in Oncology

VOLUME=Volume 15 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1534612

DOI=10.3389/fonc.2025.1534612

ISSN=2234-943X

ABSTRACT=BackgroundProstate cancer is one of the most common tumors in men, with its incidence and mortality rates continuing to rise year by year. Prostate-specific antigen (PSA) is the most commonly used screening indicator, but its lack of specificity leads to overdiagnosis and overtreatment. Therefore, identifying new biomarkers related to prostate cancer is crucial for the early diagnosis and treatment of prostate cancer.MethodsThis study utilized datasets from the Gene Expression Omnibus (GEO) to screen for differentially expressed genes (DEGs) and employed Weighted Gene Co-expression Network Analysis (WGCNA) to identify driver genes highly associated with prostate cancer within the modules. The intersection of differentially expressed genes and driver genes was taken, and Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses were performed. Furthermore, a machine learning algorithm was used to screen for core genes and construct a diagnostic model, which was then validated in an external validation dataset. The correlation between core genes and immune cell infiltration was analyzed, and Mendelian randomization (MR) analysis was conducted to identify biomarkers closely related to prostate cancer.ResultsThis study identified six core biomarkers: SLC14A1, ARHGEF38, NEFH, MSMB, KRT23, and KRT15. MR analysis demonstrated that MSMB may be an important protective factor for prostate cancer. In q-PCR experiments conducted on tumor tissues and adjacent non-cancerous tissues from prostate cancer patients, it was found that: compared to the adjacent non-cancerous tissues, the expression level of ARHGEF38 in prostate cancer tumor tissues significantly increased, while the expression levels of SLC14A1, NEFH, MSMB, KRT23, and KRT15 significantly decreased. To further validate these findings at the protein level, we conducted Western blot analysis, which corroborated the q-PCR results, demonstrating consistent expression patterns for all six biomarkers. IHC results confirmed that ARHGEF38 protein was highly expressed in tumor tissues, while MSMB expression was markedly reduced.ConclusionOur study reveals that SLC14A1, ARHGEF38, NEFH, MSMB, KRT23, and KRT15 are potential diagnostic biomarkers for prostate cancer, among which MSMB may play a protective role in prostate cancer.